TECH OFFERS

The current business dynamics have made accurate forecasts a key challenge for many companies. Predicting the market accurately is identified as a prevalent best practice to streamline critical planning processes and to achieve sales and profit growth. In order not to miss an opportunity to capture the market demand, companies need frequently reasoned and reliable forecasting results. Forecasting with only spreadsheets can no longer afford the time and manpower to meet the increasing pace of business. A smart, handy and versatile forecasting system is in urgent need for companies (including suppliers, manufacturers, distributors and retailers) willing to manage their business more efficiently. This technology offer presents an Intelligent Forecasting System that is based on Optimal Forecast Technology. The system supports companies in predicting sales, optimizing inventory, increasing cash flow, maximizing return on investment, planning marketing strategy through better visibility.

The majority of the local indoor farmers grow crops that are harvested for their leaves. One way to increase the growth rate of such leafy greens is to provide a longer period of light. However, some of the crops grown, e.g., spinach, are long-day plants that flower when the light periods are longer than their critical day-length. While important to a plant’s life cycle, this vegetative to reproductive phase change is undesirable for farmers, not only because it shortens the harvest period hence reducing the yield, but also because it changes the taste profile. To tackle this problem, a light recipe that was able to suppress flowering was formulated. Plants grown under this light recipe showed a faster growth rate than those grown under flowering-suppressing short-day photoperiod. Moreover, they do not flower even when the light period has surpassed the critical day-length. Positive results were obtained when this light recipe was tested on spinach and arugula. This technology would work for other long-day crops, and it will be beneficial to indoor farmers who are interested to try it.

Rheumatoid arthritis (RA) is a chronic, autoimmune disease that most commonly afflicts joints in the hands, wrists and knees. It arises when the immune system mistakenly targets the body’s own tissues, triggering inflammation that could lead to irreversible damage in the long run. Patients with RA often complain of swelling, pain and stiffness of the joints. In the most severe cases, RA can cause limb deformity and, ultimately, loss of function. There is no single test that can conclusively diagnose RA, and doctors typically have to run several to make sure. One such test is the X-ray, which helps visualise which joints are inflamed and to what degree. There are several methods for scoring X-ray radiographs, but most of them are manually graded, so physicians still need to attend to each image individually. As a result, interpretation of X-rays tends to be subjective and, depending on how well-staffed a hospital is, can take up to several days. RA is the most common type of autoimmune arthritis. In Singapore, around one percent of the population have RA, equivalent to tens of thousands of radiographs requiring interpretation. There is therefore a need for an automated approach of processing these X-ray images quickly without sacrificing diagnostic quality. The described invention is a deep learning algorithm that automatically scores X-ray radiographs and accurately assesses the severity of joint damage.

The most common cryopreservation media used in laboratories consist of 10% DMSO and 20% to 90% fetal bovine serum (FBS). DMSO was thought to reduce the size of ice-crystals during freezing, thus reducing mortality whereas FBS improves the viability of cells during recovery.  Although effective for cryopreservation of most cell types, FBS poised significant risks, including the possible risk of cross-species virus infections such as the “mad cow disease”, as well as the possibility of triggering an adverse immunogenic response from the recipient. Our serum-free cryopreservation formula allows the cryopreservation of mammalian cells without the need for serum.

Fall risk assessment is used to identify the risk level and risk factors, so as to prevent falls and reduce the chance of injury. Conventional assessments often involve questionnaires or functional assessment (e.g. Timed Up and Go, 4 Stage Balance) of posture, gait, cognition, and other fall risk factors. However, these clinical assessments are usually subjective and qualitative. Quantitative measurements of balance and those gait parameters could provide a more accurate and actionable insight for the elderly. However, a comprehensive analysis is usually conducted in a lab with lots of equipment such as high-speed cameras and force platforms. Also, the data collected has to be analysed by professionals. These time-consuming processes are difficult to practically integrate into typical clinic practice. A wearable, compact device was invented in order to simplify the gait analysis processes with easy access to those gait parameters. Its assessment test only takes 5 mins and can be conducted anywhere. The data collected will be associated to fall vulnerability with the help of cloud and artificial intelligence (AI). This technology enables a simple, efficient and affordable approach for elderly everywhere to benefit from early awareness and prevention of serious health risks.

The technology offers an effective and hygienic solution for capturing first void urine (typically defined as the first 20 mL of the urine flow) in a standardized volumetric way, thereby guaranteeing significantly increased analytic accuracy and patient comfort over classic urine sampling devices. The technology is highly user-friendly and allows for non-clinician dependent self-sampling in a home based setting.  Due to the non-invasive character of urine sampling, it offers an alternative to clinician-dependent sampling such as pap smear, cervico-vaginal brushes, etc.  The technology platform consists of multiple variants capturing a range of urine volumes for different application purposes. To allow for transportation and storage of the urine sample at room temperature, the technology is also available in combination with various urine stabilization chemistries that remove the need for cold-chain transportation and immediate cold storage of the collected samples.

By the year 2050, nearly 80% of the world earth’s estimate 9.8 billion population will reside in urban centers. In addition, factors such as climate change, limited arable land and pollution will make indoor farming an attractive option. Current technologies used in traditional farming will definitely be inadequate and for Singapore, it will be even more vital to achieve food security with limited resources.  The technology described herein consists of a horticultural LED system that can eventually help to address 2 main concerns of urban farming - operation cost and crop yield. The predictive algorithms can help to predict important lighting information necessary for indoor farming. This lighting information is crucial for end-user (e.g. farmers) to formulate their own “light” recipe for optimizing the growth rate of their crops. In addition, it has analytics features that can assist in analysing DLI, energy costs, etc. for improving crop yield. The technology owner is seeking partners to collaborate through various modes including technology licensing and test bedding.

Wearable devices and sensors provide a convenient and non-invasive way for continuous health monitoring. Such devices are commonly used for monitoring vital body signs such as heart rate, blood oxygen level, sleep patterns, electrocardiograms, and have been gaining popularity in the form of fitness trackers/watches (e.g. from Fitbit, Garmin and Apple). However, the current existing devices have limited capability as they are unable to track health indicators at the molecular level. To expand the functionality of existing wearable devices, we have designed a wearable prototype that allows real-time monitoring of the pH of sweat. Sweat is a biofluid that is readily available and contains rich amount of biomarkers such as sodium, chloride, pH, glucose – many of which are potential indicators for diseases, hydration level and health monitoring. In our prototype, a flexible pH sensor (developed in-house) was mounted onto the pulse oximeter of existing fitness trackers, which allows the real-time measurement of sweat pH. The pH readings were obtained readily with an accuracy of over 90%. Further, the integration of the pH sensor does not hinder the measurement of heart rate and oxygen saturation level of existing devices. This technology can potentially be extended to detect other biomarkers in sweat.

This Technology Offer allows automation of the process of monitoring stock levels in the warehouse, and when they drop below a certain threshold, trigger a certain event.  This technology can be used to address the following challenges: What items are on the shelf? What is the current inventory level? What items need to be restocked? How many items need to be restocked?